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Single nucleotide polymorphisms and pregnancy complications
Published in Moshe Hod, Vincenzo Berghella, Mary E. D'Alton, Gian Carlo Di Renzo, Eduard Gratacós, Vassilios Fanos, New Technologies and Perinatal Medicine, 2019
Federica Tarquini, Giuliana Coata, Elena Picchiassi, Gian Carlo Di Renzo
Genetic polymorphism is an existence of two or more different alleles in one locus in DNA, more often than it is expected, according to mutation frequency in a population. Both the mutation and polymorphism are a qualitative and/or quantitative change in the genetic material. SNPs are point mutations, like insertion, deletion, or substitution of one of the nucleotides in a coding or uncoding DNA sequence. These are single-letter nucleotide changes that occur in 1% or more of the population. There are 12–15 million of such variants that have been meticulously catalogued by the human genome project in the publicly available database called dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP). A wide range of methods of finding polymorphisms is now available, like a genome-wide association study (GWAS) or genome-wide linkage study (GWLS). One study reported the results of the GWAS in which, as the first, a risk locus for preeclampsia on chromosome 2q14, near the inhibin ß B (INHBB) gene was identified. In that study, researchers had successfully genotyped 648,175 SNPs in 538 preeclampsia cases and 540 normal pregnancy controls with the usage of the Illumina OmniExpress-12 BeadChip (5).
Biological data: The use of -omics in outcome models
Published in Issam El Naqa, A Guide to Outcome Modeling in Radiotherapy and Oncology, 2018
Issam El Naqa, Sarah L. Kerns, James Coates, Yi Luo, Corey Speers, Randall K. Ten Haken, Catharine M.L. West, Barry S. Rosenstein
There are no dedicated web resources for outcome modeling studies in oncology per se. Nevertheless, oncology biological markers studies can still benefit from existing bioinformatics resources for pharmacogenomic studies that contain databases and tools for genomic, proteomic, and functional analysis as reviewed by Yan [250]. For example, the National Center for Biotechnology Information (NCBI) site hosts databases such as GenBank, dbSNP, Online Mendelian Inheritance in Man (OMIM), and genetic search tools such as BLAST. In addition, the Protein Data Bank (PDB) and the program CPHmodels are useful for protein structure three-dimensional modeling. The Human Genome Variation Database (HGVbase) contains information on physical and functional relationships between sequence variations and neighboring genes. Pattern analysis using PROSITE and Pfam databases can help correlate sequence structures to functional motifs such as phosphorylation [250]. Biological pathways construction and analysis is an emerging field in computational biology that aims to bridge the gap between biomarkers findings in clinical studies with underlying biological processes. Several public databases and tools are being established for annotating and storing known pathways such as KEGG and Reactome projects or commercial ones such as the IPA or MetaCore [251]. Statistical tools are used to properly map data from gene/protein differential experiments into the different pathways such as mixed effect models [252] or enrichment analysis [253].
Detection Techniques for Single Nucleotide Polymorphisms
Published in Attila Lorincz, Nucleic Acid Testing for Human Disease, 2016
W. Mathias Howell, Johan Stenberg, Chatarina Larsson, Mats Nilsson, Ulf Landegren
These SNPs and additional polymorphisms from many other private and public groups can be found in an online database dbSNP (http://www.ncbi.nlm.nih.gov/projects/SNP/). This collection of sequence variants is freely accessible and contains information about SNPs and other types of genetic polymorphisms relevant to the study of human disease, for example, microsatellites and insertions/deletions (indels).15 Single nucleotide polymorphisms are by far the most abundant (Figure 6.2). As of the writing of this chapter, there are almost 9.5 million SNP entries in dbSNP (build 124). Roughly half have been validated from at least two independent sources or using two genotyping methods. About a million of these SNP entries include allele frequency information from at least one population. HGVBase (http://hgvbase.cgb.ki.se)16,17 is another online repository of polymorphisms that is highly curated and focuses on the relationship of phenotype and DNA variation. Additionally, a number of SNP and mutation databases centered on specific genes or diseases can also be found on the Web (Table 6.1).
Associations between WNT signaling pathway-related gene polymorphisms and risks of osteoporosis development in Chinese postmenopausal women: a case–control study
Published in Climacteric, 2022
Z. Yang, J. Liu, J. Fu, S. Li, Z. Chai, Y. Sun
The genomic DNA was isolated from whole blood using a DNA extraction kit (GoldMag Co. Ltd, Xi′an, China) and the DNA concentration was measured by a Nanodrop 2000 (Thermo Scientific, Waltham, MA, USA) in this study. As for the SNP selection, four WNT16 SNPs (rs3779381, rs3801387, rs917727 and rs7776725) and three LRP5 SNPs (rs2291467, rs11228240 and rs12272917) were selected with minor allele frequency (MAF) > 0.05 in a Chinese Han in Beijing population based on data from the 1000 Genomes Project (http://www.internationalgenome.org/) for further genotyping analysis in this study. Besides, SNPs rs3779381, rs3801387, rs917727 and rs7776725 and SNPs rs2291467 and rs11228240 were in high-linkage disequilibrium in WNT16 and LRP5, respectively. Additionally, after annotation analysis using HaploReg v4, all of these SNPs were selected expression quantitative trait loci (eQTL) hits (Supplemental Table 2). Furthermore, the dbSNP (https://www.ncbi.nlm.nih.gov/snp/) was also used for analyzing the position of these selected SNPs. Last but not least, the GWAS research status of these SNPs was also considered in the SNP selection [15–18]. The SNP genotyping was carried out using the MassARRAY iPLEX system (Agena Bioscience, San Diego, CA, USA). The primers for this study were designed using Agena MassARRAY Assay Design 3.0 software (Supplemental Table 1). We managed and analyzed the data on Agena Typer 4.0 software.
Genetic variations in the human immune system influence susceptibility to tegumentary leishmaniasis: a systematic review and meta-analysis
Published in Expert Review of Clinical Immunology, 2021
Daniele Stéfanie Sara Lopes Lera-Nonose, Larissa Ferreira De Oliveira, Aline Brustolin, Thais Silva Santos, Jully Oyama, Áquila Carolina Fernandes Herculano Ramos-Milaré, Mariana De Souza Terron-Monich, Izabel Galhardo Demarchi, Quirino Alves De Lima Neto, Jorge Juarez Vieira Teixeira, Maria Valdrinez Campana Lonardoni
Before the full-text review, articles were screened by titles and abstracts (DSSLLN, AAB, TS, and LFO). The full-text analysis was conducted individually by each researcher. The references of all included articles were searched for additional records. All disagreements were resolved by establishing a consensus. During all processes, the researchers were blinded to each other’s decisions. The information was extracted by four researchers (DSSLLN, AAB, TS, and LFO) and confirmed by a group of experts in duplicate (JO, ACFHRM, MVCL, and MSTM). Complementary data on polymorphisms, when not available, were searched on the NCBI-dbSNP (Single Nucleotide Polymorphism Database) and Ensemble database. The wild allele in a specific SNP was defined according to the NCBI-dbSNP to homogenize the studies in different populations. To standardize the polymorphism description, we used the dbSNP Reference SNP number provided by NCBI-dbSNP when possible.
Differential mitochondrial genome in patients with Rheumatoid Arthritis
Published in Autoimmunity, 2021
Kumar Sagar Jaiswal, Shweta Khanna, Arup Ghosh, Prasanta Padhan, Sunil Kumar Raghav, Bhawna Gupta
A total of 382 SNPs variants were observed in mtDNA samples isolated from our case control cohort of 23 RA patients and 17 HCs when analysed against mtDNA sequences from rCRS repository shown in Figure 1. Supplementary Table S2 shows the location of SNPs, reference and altered alleles, gene names, type of variants, variant impact, amino acid changes due to SNP, disease score, dbSNP ID, function of observed SNPs, annotated disease, and the frequency of SNPs in controls and patients with a statistical significance (Fisher’s exact test p value). Among the 382 SNPs identified, 9 variants were present in all 40 mtDNA samples (17 HC and 23 RA samples) at positions 73 A > G and 263 A > G in intergenic region; 750 A > G and 1438 A > G in RNR1 gene; 4769 A > G in ND2 gene; 7028 C > T in COX1; 8860 A > G in ATP6; 11,719 G > A in ND4 gene and 14,766 C > T in CYTB gene (Supplementary Table S2). This shows a difference in mtDNA sequence in Indian population in comparison to the Caucasian mtDNA sequence deposited in the rCRS database.